SoftLab-NSK develops complete TV broadcast automation solutions that work with the 4K format and HEVC compression and include functionality for video encoding. When the company wanted to expand its ﬂagship Forward T* line of playout servers, it needed the most efficient solution for video transcoding.
This blog post is part of a series that describes my summer school project at CERN openlab. In the first post we introduced the problem of track reconstruction and the track seeds filtering. Today we are going to discuss the model architecture and the results.
In a world becoming ever more attuned to potential security threats, the need to deploy sophisticated surveillance systems is increasing. An intellectual system that functions as an intuitive “robotic eye” for accurate, real-time detection of unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public areas. This article discusses inferencing a Microsoft Common Objects in Context (MS-COCO) detection model for detecting unattended baggage in a train station.
Machine learning applications are very compute intensive by their nature. That is why optimization for performance is quite important for them. One of the most popular libraries, Tensorflow*, already has an embedded timeline feature that helps understand which parts of the computational graph are causing bottlenecks but it lacks some advanced features like an architectural analysis.
Finding vulnerabilities in code is part of the constant security game between attackers and defenders. An attacker only needs to find one opening to be successful, while a defender needs to search for and plug all or at least most of the holes in a system. Thus, a defender needs more effective tools than the attacker to come out ahead.